Allocation of simulation runs for simulation optimization

Simulation optimization (SO) is the process of finding the optimum design of a system whose performance measure(s) are estimated via simulation. We propose some ideas to improve overall efficiency of the available SO methods and develop a new approach that primarily deals with continuous two dimensional problems with bounded feasible region. Our search based method, called Adaptive Partitioning Search (APS), uses a neural network as metamodel and combines various exploitation strategies to locate the optimum. Our numerical results show that in terms of the number of evaluations (simulation runs) needed, the APS algorithm converges much faster to the optimum design than two well established methods used as benchmark.